Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.

<h4>Background</h4>As war and famine are population level stressors that have been historically linked to menstrual cycle abnormalities, we hypothesized that the COVID-19 pandemic could similarly affect ovulation and menstruation among women.<h4>Methodology</h4>We conducted a...

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Autores principales:	Brian T Nguyen, Raina D Pang, Anita L Nelson, Jack T Pearson, Eleonora Benhar Noccioli, Hana R Reissner, Anita Kraker von Schwarzenfeld, Juan Acuna
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f307979538da4081a84202b7accd6bc1
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spelling	oai:doaj.org-article:f307979538da4081a84202b7accd6bc12021-12-02T20:07:49ZDetecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.1932-620310.1371/journal.pone.0258314https://doaj.org/article/f307979538da4081a84202b7accd6bc12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258314https://doaj.org/toc/1932-6203<h4>Background</h4>As war and famine are population level stressors that have been historically linked to menstrual cycle abnormalities, we hypothesized that the COVID-19 pandemic could similarly affect ovulation and menstruation among women.<h4>Methodology</h4>We conducted a retrospective cohort study examining changes in ovulation and menstruation among women using the Natural Cycles mobile tracking app. We compared de-identified cycle data from March-September 2019 (pre-pandemic) versus March-September 2020 (during pandemic) to determine differences in the proportion of users experiencing anovulation, abnormal cycle length, and prolonged menses, as well as population level changes in these parameters, while controlling for user-reported stress during the pandemic.<h4>Findings</h4>We analyzed data from 214,426 cycles from 18,076 app users, primarily from Great Britain (29.3%) and the United States (22.6%). The average user was 33 years of age; most held at least a university degree (79.9%). Nearly half (45.4%) reported more pandemic-related stress. Changes in average cycle and menstruation lengths were not clinically significant, remaining at 29 and 4 days, respectively. Approximately 7.7% and 19.5% of users recorded more anovulatory cycles and abnormal cycle lengths during the pandemic, respectively. Contrary to expectation, 9.6% and 19.6% recorded fewer anovulatory cycles and abnormal cycle lengths, respectively. Women self-reporting more (32.0%) and markedly more (13.6%) stress during the pandemic were not more likely to experience cycle abnormalities.<h4>Conclusions</h4>The COVD-19 pandemic did not induce population-level changes to ovulation and menstruation among women using a mobile app to track menstrual cycles and predict ovulation. While some women experienced abnormalities during the pandemic, this proportion was smaller than that observed prior to the pandemic. As most app users in this study were well-educated women over the age of 30 years, and from high-income countries, their experience of the COVID-19 pandemic might differ in ways that limit the generalizability of these findings.Brian T NguyenRaina D PangAnita L NelsonJack T PearsonEleonora Benhar NoccioliHana R ReissnerAnita Kraker von SchwarzenfeldJuan AcunaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258314 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brian T Nguyen Raina D Pang Anita L Nelson Jack T Pearson Eleonora Benhar Noccioli Hana R Reissner Anita Kraker von Schwarzenfeld Juan Acuna Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.
description	<h4>Background</h4>As war and famine are population level stressors that have been historically linked to menstrual cycle abnormalities, we hypothesized that the COVID-19 pandemic could similarly affect ovulation and menstruation among women.<h4>Methodology</h4>We conducted a retrospective cohort study examining changes in ovulation and menstruation among women using the Natural Cycles mobile tracking app. We compared de-identified cycle data from March-September 2019 (pre-pandemic) versus March-September 2020 (during pandemic) to determine differences in the proportion of users experiencing anovulation, abnormal cycle length, and prolonged menses, as well as population level changes in these parameters, while controlling for user-reported stress during the pandemic.<h4>Findings</h4>We analyzed data from 214,426 cycles from 18,076 app users, primarily from Great Britain (29.3%) and the United States (22.6%). The average user was 33 years of age; most held at least a university degree (79.9%). Nearly half (45.4%) reported more pandemic-related stress. Changes in average cycle and menstruation lengths were not clinically significant, remaining at 29 and 4 days, respectively. Approximately 7.7% and 19.5% of users recorded more anovulatory cycles and abnormal cycle lengths during the pandemic, respectively. Contrary to expectation, 9.6% and 19.6% recorded fewer anovulatory cycles and abnormal cycle lengths, respectively. Women self-reporting more (32.0%) and markedly more (13.6%) stress during the pandemic were not more likely to experience cycle abnormalities.<h4>Conclusions</h4>The COVD-19 pandemic did not induce population-level changes to ovulation and menstruation among women using a mobile app to track menstrual cycles and predict ovulation. While some women experienced abnormalities during the pandemic, this proportion was smaller than that observed prior to the pandemic. As most app users in this study were well-educated women over the age of 30 years, and from high-income countries, their experience of the COVID-19 pandemic might differ in ways that limit the generalizability of these findings.
format	article
author	Brian T Nguyen Raina D Pang Anita L Nelson Jack T Pearson Eleonora Benhar Noccioli Hana R Reissner Anita Kraker von Schwarzenfeld Juan Acuna
author_facet	Brian T Nguyen Raina D Pang Anita L Nelson Jack T Pearson Eleonora Benhar Noccioli Hana R Reissner Anita Kraker von Schwarzenfeld Juan Acuna
author_sort	Brian T Nguyen
title	Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.
title_short	Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.
title_full	Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.
title_fullStr	Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.
title_full_unstemmed	Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.
title_sort	detecting variations in ovulation and menstruation during the covid-19 pandemic, using real-world mobile app data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f307979538da4081a84202b7accd6bc1
work_keys_str_mv	AT briantnguyen detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT rainadpang detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT anitalnelson detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT jacktpearson detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT eleonorabenharnoccioli detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT hanarreissner detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT anitakrakervonschwarzenfeld detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata AT juanacuna detectingvariationsinovulationandmenstruationduringthecovid19pandemicusingrealworldmobileappdata
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Detecting variations in ovulation and menstruation during the COVID-19 pandemic, using real-world mobile app data.

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